In today’s business landscape, AI integration is not just for tech companies. Any business can become an AI business by using AI for automation and workflow enhancement. However, simply fine-tuning existing large language models (LLMs) isn’t enough. A survey found that 90% of businesses require more than just fine-tuning to effectively integrate AI. This involves customizing LLMs to meet specific business needs, which includes handling vast amounts of data for swift and accurate responses. AI can manifest in various forms, from visible chatbots to invisible algorithms that power e-commerce. For instance, in the travel industry, AI can process multiple customer queries simultaneously, reducing response times significantly. The market offers various LLMs like ChatGPT, Claude, and Grok, but fine-tuning alone doesn’t suffice. Businesses must also incorporate new data through retrieval-augmented generation (RAG), which updates AI models with the latest information. However, traditional vector-based RAG systems often fail to deliver precise data, leading to less accurate AI responses. A newer approach, knowledge maps, is gaining traction. Knowledge maps, used by companies like CLOVA X and Trustbit, provide a more structured data extraction process, enhancing AI accuracy and relevance. This method allows for continuous improvement, adapting to new business demands and benchmarks.
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